Egregiously misrepresented course. "No previous experience necessary" is false. The instructor often states, "when you originally learned linear algebra" throughout the course. This course is designed to teach students who already know linear algebra how to apply it to machine learning.

If you do not already know linear algebra, the completion time of "15 hours (approximately)" is not realistic. This course took me 4-5 times as long as was advertised. I did not have 60-75 hours to complete this course. the advertised 3 hours a week was reasonable, the reality of 12-15 hours a week caused me significant inconvenience and frustration.

Unfortunately not finding it very engaging. I have a bachelors of science in chemical engineering earned 20 years ago, so this isn't virgin ground, but I'm finding it's not making intuitive sense (having a 2nd run at it means going past the "rote learning" approach of 20 years ago). I find 3Blue1Brown more motivating (you start to get it, you search to go further, etc). Also the clear board approach with the instructor's body in the background creates a lot of visual interference in my opinion. Blackboards and whiteboards were less distracting. A similar approach could have been used here.

Authors try to teach babies. Might be good, it is hard to judge for me as I know linear algebra. Definitely boring to me. For example 3Blue1Brown (which they reference btw) is ingenious in my opinion, so it might be not me who is the problem.

But the quizzes just don't make sense! The ones where solving problems involved might have 2 numerically right answers but only one of two is treated as the right. And there are just idiotic or not covered in lectures answers for quizzes without problems.

Do not bother with this course. There are some amazing free resources on youtube (3 blue 1 brown, and rootmath are two). This is poorly explained, the grading system is ridiculous and buggy, there is zero support from the tutors. Worst Coursera course I have ever taken.

Hands down the worst course I've ever taken in my life! Poorly put together and extremely short videos that don't provide an adequate amount of knowledge especially in relationship to the given quizzes. I truly hope this course is removed.

Why is this course so highly rated?

It's more vague than a college girl's interrogation transcript.

Go learn from MIT OpenCourseware or Khan Academy, this one is absolute junk.

It was a terrible experince, bo readings no lectures material nothing. the videos are confusing and in most of the cases they don't give you teh genereal rule to apply.

DONT take this course !! NOT worth for moeny !! you will need to spend additional 30 hours to search on Web and Youtube to understand the cncept in order to get pass

Quite difficult for learner who didn't know about linear algebra.It jump and few example and skip a lot of part for understand.But good for recall.

It teaches simple math, very briefly,

and make test that needs Python and other Math basic knowledges.

It isn't kind for beginner!

In lesson about vectors (first lesson in this course) teacher is talking about gause distribution and other difficult things

There are a lot of problems in lab and we cannot submit our assignment just a waste of waste

It is disrespectful that programming assignments are not accessible for audit users.

Concepts were not well explained, and understanding Python is a requirement.

not a good course to start, assumes you already knwo the concepts!

Lab exercises are unrelated to the lectures.

it is not in with greek subtitle

It is not at all for biggners.

bad very bad

very bad

So bad

It's an amazing course but apart from the feedback that I have in the post-course survey - I would also like to share the following things.

1) In the quiz on 'Eigenvalues and eigenvectors' in the Week-5 module -- I personally faced a lot of problems in completing the quiz. I understand the concepts pretty well from the lectures but still, I could not figure out the questions in this particular quiz. Particularly the questions about finding the effect of using a particular Link matrix on the eigenvectors. These questions were not easy to answer as intuitively speaking I did not learn how to interpret the meaning of different values in the eigenvectors matrix to answer those questions forex. it makes the eigenvalues small or It makes the eigenvalues we are looking for larger.

Overall - it is a really useful and much-required course to fill in the gap between the mathematical fundamentals and the practice of machine learning. I am glad that the Professors came up with this idea to design this course.

Thanks for this wonderful learning opportunity! This is the first math course that I've succesfully completed and I'm quite proud of myself. Teaching material is very clear and nicely structured - I've shared the first video about Eigenvectors and Eigenvalues with many people: it's illuminating -; also, quizzes and programming assignments really helped in understanding some of the main concepts. Actually, the nice balance among theory, hand-made calculations, geometrical examples and programming assignments provides a valuable way to learn from multiple perspectives.

Apart from this. I realize that I still have to completely "digest" some of the concepts that have been introduced here. In other words, I've learned to use them decently but for sure I need to reharse some of them in order to fully understand their meaning. But, by the way, this is fair enough: linear algebra is a complex and deep subject and five weeks are probably not enough to fully understand its main principles.

In reviewing this course it is important to state what this course is and what it is not. It is not an in-depth formal introduction to the mathematics of linear algebra. For those who are looking for that, the course simply does not deliver. Secondly, while it is technically possible to complete this course without any beforehand knowledge of the topic, I think this would be incredibly challenging to do. Indeed, the course is not intended to be a first primer in linear algebra. The ease with which the instructors just juggle the cosine rule, or calculate the inverse of a 2 by 2 matrix indicates that they do assume you know such things. So also absolute beginners will be disappointed with this course. However, if you have had linear algebra in your past, and you are using this course to refresh your mind, it is absolutely brilliant. I can confidently say that nobody has ever presented this material to me in as intuitive a way. A well deserved five stars from me.

The course touches base in the main topics of Linear Algebra utilized in ML. I took this course at the same time with a ML one. I hadn't had linear algebra in University and the contents of this course helped me get through many bottlenecks in the ML course (which obviously takes for granted Algebra).

The videos are very well produced, animations look great and explanations are crystal clear.

The instructors present the topics in a friendly way and always keep the focus on the concepts rather than on manual calculations (of which you won't be doing much in ML...).

The only downside is that watching videos more slowly (0.75x) is not fluent. I don't know if the issue is on my end or if the videos don't have enough frames per second.

If you have no Linear Algebra basis and are planning to do ML, It's definetely worth it taking this course.